This is a correction article to a study on fusion of multimodal ECG and biometric data for improved detection of obstructive sleep apnea hypopnea syndrome (OSAHS).[1][5] The original study proposed a cheaper and more accurate way to detect OSAHS using electrocardiogram (ECG) and biometric data.[5] The model uses data fusion from multiple signals including ECG, EEG, EOG, SpO2, CO2 and respiratory signals.[2][3] The signals were resampled to a frequency of 128 Hz and divided into 30-second non-overlapping epochs.[2][3] The proposed model achieves high performance with AUROC > 0.9 even with high noise or missing modalities.[2][3] The model outperforms existing methods in apnea detection with various combinations of available data.[2][3] The fusion of multimodal data increases the robustness of the system compared to unimodal data.[2][3]